PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.
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Updated
Aug 26, 2020 - Python
PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.
Implementation of Conv-based and Vit-based networks designed for CIFAR.
The aim of this project is to train autoencoder, and use the trained weights as initialization to improve classification accuracy with cifar10 dataset.
contains exercise solution
ConvMixer - Patches Are All You Need?
Designed a smaller architecture implemented from the paper Deep Residual Learning for Image Recognition and achieved 93.65% accuracy.
Implemeting SVM to classify images with hinge loss and the softmax loss.
The cifar10 classification project completed by tensorflow, including complete training, prediction, visualization, independent of each module of the project, and convenient expansion.
⭐ Make Once for All support CIFAR10 dataset.
Classification of CIFAR dataset with CNN which has %91 accuracy and deployment of the model with FLASK.
Implementing a neural network classifier for cifar-10
Deep Learning Projects
Implemented the Deep Residual Learning for Image Recognition Paper and achieved better accuracy by customizing different parts of the architecture.
A summarization of the course Deep Learning with PyTorch at Jovian.
Classifies the cifar-10 database by using a vgg16 network. Training, predicting and showing learned filters are included.
CapsNet models
the CIFAR10 dataset
使用了 https://github.com/SaeedShurrab/SimSiam-pytorch 作为Simsiam backbone,添加了中文注释和简单的训练过程
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